Natural Language Processing is an active and emerging field of research in the computer sciences. Within it is the subfield of text simplification which is aimed towards teaching the computer the so far primarily manual task of simplifying text, efficiently. While handcrafted systems using syntactic techniques were the first simplification systems, Recurrent Neural Networks and Long Short Term Memory networks employed in seq2seq models with attention were considered state-of-the-art until very recently when the transformer architecture which did away with the computational problems that plagued them. This paper presents our work on simplification using the transformer architecture in the process of making an end-to-end simplification system for linguistically complex reference books written in English and our findings on the drawbacks/limitations of the transformer during the same. We call these drawbacks as the Fact Illusion Induction, Named Entity Problem and Deep Network Problem and try to theorize the possible reasons for them.

Artificial Intelligence, Natural Language Processing, Neural Networks, Text Simplification, Transformer

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